ArticleMedical image analysis2017
Integrated local binary pattern texture features for classification of breast tissue imaged by optical coherence microscopy.
Article in Medical image analysis, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 53 citations in OpenAlex.
- Differentiating triple-negative breast cancer and atypical fibroadenomas using an ultrasound-based radiomics nomogram.BMC medical imaging · 2025Article
- Deep learning radiomics based prediction of axillary lymph node metastasis in breast cancer.NPJ breast cancer · 2024Article
- Rapid On-Site AI-Assisted Grading for Lung Surgery Based on Optical Coherence Tomography.Cancers · 2023Article
- Multimodal Raman spectroscopy and optical coherence tomography for biomedical analysis.Journal of biophotonics · 2023Review
- Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy.Health information science and systems · 2022Article
- Automated assessment of breast margins in deep ultraviolet fluorescence images using texture analysis.Biomedical optics express · 2022Article
- Classification of oral salivary gland tumors based on texture features in optical coherence tomography images.Lasers in medical science · 2022Article
- An Automatic Classification Method on Chronic Venous Insufficiency Images.Scientific reports · 2018Article
- Optical coherence tomography and computer-aided diagnosis of a murine model of chronic kidney disease.Journal of biomedical optics · 2017Article
- Involvement of Machine Learning for Breast Cancer Image Classification: A Survey.Computational and mathematical methods in medicine · 2017Review
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
Abstract
This paper proposes a texture analysis technique that can effectively classify different types of human breast tissue imaged by Optical Coherence Microscopy (OCM). OCM is an emerging imaging modality for rapid tissue screening and has the potential to provide high resolution microscopic images that approach those of histology. OCM images, acquired without tissue staining, however, pose unique challenges to image analysis and pattern classification. We examined multiple types of texture features and found Local Binary Pattern (LBP) features to perform better in classifying tissues imaged by OCM. In order to improve classification accuracy, we propose novel variants of LBP features, namely average LBP (ALBP) and block based LBP (BLBP). Compared with the classic LBP feature, ALBP and BLBP features provide an enhanced encoding of the texture structure in a local neighborhood by looking at intensity differences among neighboring pixels and among certain blocks of pixels in the neighborhood. Fourty-six freshly excised human breast tissue samples, including 27 benign (e.g. fibroadenoma, fibrocystic disease and usual ductal hyperplasia) and 19 breast carcinoma (e.g. invasive ductal carcinoma, ductal carcinoma in situ and lobular carcinoma in situ) were imaged with large field OCM with an imaging area of 10 × 10 mm
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.